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AI-Powered Mobile Application for Retail Product Tracking and Shelf Analytics

brights.io

Consumer products & services

Retail

Challenge in Retail Product Placement Monitoring

Inefficient manual tracking of product placement and stock status across retail locations leads to potential revenue loss, compliance issues, and suboptimal shelf space utilization for FMCG products.

About the Client

Producer and distributor of tobacco products seeking enhanced retail shelf monitoring capabilities

Key Project Goals

Develop AI-driven system for automated SKU recognition using mobile devices
Implement real-time product presence tracking across multiple retail locations
Generate detailed compliance reports for sales point inventory status
Improve accuracy of shelf stock monitoring to 98%+ through computer vision

Core System Capabilities

Real-time product recognition via convolutional neural networks (CNN)
Automated stock status tracking using device camera input
OpenCV-based template matching for SKU pattern detection
Multi-SKU reporting dashboard with location-specific analytics
Scalable AI model supporting 50+ product types

Technology Stack Requirements

OpenCV for computer vision processing

TensorFlow/PyTorch for CNN implementation

iOS Swift for mobile application development

Cloud-based model training infrastructure

System Integration Needs

Existing inventory management systems
Retail analytics platforms
Cloud storage for image processing workflows

Performance and Quality Standards

99.9% system uptime for field operations
Sub-200ms image processing latency
GDPR-compliant data handling
Scalable architecture for 10,000+ concurrent users

Expected Business Outcomes

Implementation of AI-powered shelf monitoring will reduce manual audit time by 70%, increase product availability compliance by 40%, and provide actionable insights for optimizing retail space utilization across 500+ sales points.